To address the large discrepancy between consensus quality standards and healthcare services actually delivered to those eligible, the Institute of Medicine has called for new quality initiatives and has identified asthma as one if its priority areas. Each year, thousands of preventable deaths are attributed to asthma. The asthma-related health burden is especially pronounced in vulnerable populations. Opportunities to improve asthma care quality hinge on the capacity to comprehensively and routinely assess the care that is actually delivered to asthma patients. Current quality measurement studies require extensive clinical chart review and are not cost-effective on a public health scale. Healthcare information technology (Health IT) could have a big impact by enabling automated quality assessments. Electronic medical record (EMR) systems capture clinical information that could make comprehensive, cost effective quality assessment possible. However, much relevant information (and perhaps 50% or more of the necessary data) resides in free-text clinical notes, which presents a significant challenge to automated quality assessment. This research aims to develop, validate, apply, and evaluate a scalable method for routine and comprehensive measurement of outpatient asthma care quality. To accomplish this, we will extend MediClass (a "Medical Classifier"), which is a proven technology for extracting care quality data from both coded data and free-text clinical notes in the EMR. This research will perform retrospective analysis of EMR data from two distinct health systems: a mid-sized HMO (Kaiser Permanente Northwest, KPNW) and a consortium of public health clinics (Oregon Community Health Information Network) including a diverse sample of patients, providers, and health care practices of the Pacific Northwest. This project leverages Health IT to assess and improve quality of care for both insured and the indigent, uninsured, and underinsured populations of this region. We propose to use an outpatient Asthma Care Quality (ACQ) measure set derived from the original RAND Quality Assessment Tools Project. We will first refine the ACQ measures using up-to-date clinical guidelines and consensus standards for outpatient diagnosis, treatment and management of asthma in patients older than 5 years. Next, we will develop and validate an automated method for applying these measures to comprehensive EMR data. At each study site, the MediClass system will extract coded data and use natural language processing (NLP) on free-text clinical notes to identify ACQ-relevant clinical events in the electronic medical record. Then, we will apply the ACQ measures to assess current levels of asthma care quality in the two health systems. Finally, we will evaluate the association between ACQ measures of recommended asthma care and actual clinical outcomes using extensive longitudinal patient data available at the KPNW study site. [unreadable] [unreadable] [unreadable]